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DepthAI Python API utilities, examples, and tutorials.

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DepthAI Demo Program

This repo contains demo application, which can load different networks, create pipelines, record video, etc.

Documentation is available at

Python modules (Dependencies)

DepthAI Demo requires numpy, opencv-python and depthai. To get the versions of these packages you need for the program, use pip: (Make sure pip is upgraded:

python3 -m pip install -U pip

Optional: For command line autocomplete when pressing TAB, only bash interpreter supported now: Add to .bashrc:

echo 'eval "$(register-python-argcomplete"' >> ~/.bashrc

If you use any other interpreter:


- depth & CNN inference example

Conversion of existing trained models into Intel Movidius binary format

OpenVINO toolkit contains components which allow conversion of existing supported trained

models into Intel Movidius binary format through the Intermediate Representation (IR) format.

Example of the conversion: 1. First the

tool will convert the model to IR format:
   cd /deployment_tools/model_optimizer
   python3 --model_name ResNet50 --output_dir ResNet50_IR_FP16 --framework tf --data_type FP16 --input_model inference_graph.pb

  • The command will produce the following files in the ResNet50_IR_FP16 directory:
    • ResNet50.bin - weights file;
    • ResNet50.xml - execution graph for the network;
    • ResNet50.mapping - mapping between layers in original public/custom model and layers within IR.
  1. The weights (

    ) and graph (
    ) files produced above (or from the Intel Model Zoo) will be required for building a blob file, with the help of the
    tool. When producing blobs, the following constraints must be applied:
  2. CMX-SLICES = 4 SHAVES = 4 INPUT-FORMATS = 8 OUTPUT-FORMATS = FP16/FP32 (host code for meta frame display should be updated accordingly)

    Example of command execution:

    /deploymenttools/inferenceengine/lib/intel64/myriadcompile -m ./ResNet50.xml -o ResNet50.blob -ip U8 -VPUMYRIADPLATFORM VPUMYRIAD2480 -VPUNUMBEROFSHAVES 4 -VPUNUMBEROFCMXSLICES 4

Reporting issues

We are actively developing the DepthAI framework, and it's crucial for us to know what kind of problems you are facing.
If you run into a problem, please follow the steps below and email [email protected]:

  1. Run
    and share the output from (
  2. Take a photo of a device you are using (or provide us a device model)
  3. Describe the expected results;
  4. Describe the actual running results (what you see after started your script with DepthAI)
  5. How you are using the DepthAI python API (code snippet, for example)
  6. Console output

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